Overview

Dataset statistics

Number of variables31
Number of observations1913
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.2 KiB
Average record size in memory256.0 B

Variable types

Numeric22
Categorical8
DateTime1

Timeseries statistics

Number of series0
Time series length1913
Starting point2011-01-29 00:00:00
Ending point2016-04-24 00:00:00
Period1 day
2024-01-03T22:35:44.708606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:44.766479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Alerts

HOBBIES_1_007_CA_1_validation is highly imbalanced (58.4%)Imbalance
HOBBIES_1_011_CA_1_validation is highly imbalanced (80.6%)Imbalance
HOBBIES_1_013_CA_1_validation is highly imbalanced (60.4%)Imbalance
HOBBIES_1_018_CA_1_validation is highly imbalanced (86.5%)Imbalance
HOBBIES_1_020_CA_1_validation is highly imbalanced (54.6%)Imbalance
HOBBIES_1_022_CA_1_validation is highly imbalanced (53.0%)Imbalance
HOBBIES_1_026_CA_1_validation is highly imbalanced (94.7%)Imbalance
date_col has unique valuesUnique
HOBBIES_1_001_CA_1_validation has 1492 (78.0%) zerosZeros
HOBBIES_1_002_CA_1_validation has 1511 (79.0%) zerosZeros
HOBBIES_1_003_CA_1_validation has 1697 (88.7%) zerosZeros
HOBBIES_1_004_CA_1_validation has 617 (32.3%) zerosZeros
HOBBIES_1_005_CA_1_validation has 950 (49.7%) zerosZeros
HOBBIES_1_006_CA_1_validation has 1261 (65.9%) zerosZeros
HOBBIES_1_008_CA_1_validation has 535 (28.0%) zerosZeros
HOBBIES_1_009_CA_1_validation has 1033 (54.0%) zerosZeros
HOBBIES_1_010_CA_1_validation has 996 (52.1%) zerosZeros
HOBBIES_1_014_CA_1_validation has 852 (44.5%) zerosZeros
HOBBIES_1_015_CA_1_validation has 373 (19.5%) zerosZeros
HOBBIES_1_016_CA_1_validation has 393 (20.5%) zerosZeros
HOBBIES_1_017_CA_1_validation has 963 (50.3%) zerosZeros
HOBBIES_1_019_CA_1_validation has 995 (52.0%) zerosZeros
HOBBIES_1_021_CA_1_validation has 1155 (60.4%) zerosZeros
HOBBIES_1_023_CA_1_validation has 896 (46.8%) zerosZeros
HOBBIES_1_024_CA_1_validation has 1573 (82.2%) zerosZeros
HOBBIES_1_025_CA_1_validation has 1360 (71.1%) zerosZeros
HOBBIES_1_027_CA_1_validation has 1650 (86.3%) zerosZeros
HOBBIES_1_028_CA_1_validation has 1068 (55.8%) zerosZeros
HOBBIES_1_029_CA_1_validation has 631 (33.0%) zerosZeros
HOBBIES_1_030_CA_1_validation has 930 (48.6%) zerosZeros

Reproduction

Analysis started2024-01-03 21:34:59.604072
Analysis finished2024-01-03 21:35:44.696230
Duration45.09 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

HOBBIES_1_001_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31364349
Minimum0
Maximum5
Zeros1492
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:44.864658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68525028
Coefficient of variation (CV)2.1848063
Kurtosis8.3401167
Mean0.31364349
Median Absolute Deviation (MAD)0
Skewness2.6687908
Sum600
Variance0.46956794
MonotonicityNot monotonic
2024-01-03T22:35:44.962467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1492
78.0%
1 291
 
15.2%
2 94
 
4.9%
3 25
 
1.3%
4 9
 
0.5%
5 2
 
0.1%
ValueCountFrequency (%)
0 1492
78.0%
1 291
 
15.2%
2 94
 
4.9%
3 25
 
1.3%
4 9
 
0.5%
5 2
 
0.1%
ValueCountFrequency (%)
5 2
 
0.1%
4 9
 
0.5%
3 25
 
1.3%
2 94
 
4.9%
1 291
 
15.2%
0 1492
78.0%

HOBBIES_1_002_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2577104
Minimum0
Maximum5
Zeros1511
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.037247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56942163
Coefficient of variation (CV)2.2095407
Kurtosis11.781735
Mean0.2577104
Median Absolute Deviation (MAD)0
Skewness2.9064956
Sum493
Variance0.32424099
MonotonicityNot monotonic
2024-01-03T22:35:45.112810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1511
79.0%
1 338
 
17.7%
2 44
 
2.3%
3 15
 
0.8%
4 3
 
0.2%
5 2
 
0.1%
ValueCountFrequency (%)
0 1511
79.0%
1 338
 
17.7%
2 44
 
2.3%
3 15
 
0.8%
4 3
 
0.2%
5 2
 
0.1%
ValueCountFrequency (%)
5 2
 
0.1%
4 3
 
0.2%
3 15
 
0.8%
2 44
 
2.3%
1 338
 
17.7%
0 1511
79.0%

HOBBIES_1_003_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15054888
Minimum0
Maximum6
Zeros1697
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.177958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48655669
Coefficient of variation (CV)3.2318852
Kurtosis27.75202
Mean0.15054888
Median Absolute Deviation (MAD)0
Skewness4.4682401
Sum288
Variance0.23673741
MonotonicityNot monotonic
2024-01-03T22:35:45.245999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1697
88.7%
1 166
 
8.7%
2 34
 
1.8%
3 13
 
0.7%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
0 1697
88.7%
1 166
 
8.7%
2 34
 
1.8%
3 13
 
0.7%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 1
 
0.1%
4 1
 
0.1%
3 13
 
0.7%
2 34
 
1.8%
1 166
 
8.7%
0 1697
88.7%

HOBBIES_1_004_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7187663
Minimum0
Maximum15
Zeros617
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.320515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9898666
Coefficient of variation (CV)1.1577296
Kurtosis6.192527
Mean1.7187663
Median Absolute Deviation (MAD)1
Skewness2.0210051
Sum3288
Variance3.9595692
MonotonicityNot monotonic
2024-01-03T22:35:45.400493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 617
32.3%
1 473
24.7%
2 348
18.2%
3 201
 
10.5%
4 107
 
5.6%
5 75
 
3.9%
6 38
 
2.0%
7 16
 
0.8%
8 15
 
0.8%
10 8
 
0.4%
Other values (6) 15
 
0.8%
ValueCountFrequency (%)
0 617
32.3%
1 473
24.7%
2 348
18.2%
3 201
 
10.5%
4 107
 
5.6%
5 75
 
3.9%
6 38
 
2.0%
7 16
 
0.8%
8 15
 
0.8%
9 4
 
0.2%
ValueCountFrequency (%)
15 1
 
0.1%
14 2
 
0.1%
13 1
 
0.1%
12 4
 
0.2%
11 3
 
0.2%
10 8
 
0.4%
9 4
 
0.2%
8 15
 
0.8%
7 16
0.8%
6 38
2.0%

HOBBIES_1_005_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96654469
Minimum0
Maximum9
Zeros950
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.482533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2944722
Coefficient of variation (CV)1.3392782
Kurtosis4.5146395
Mean0.96654469
Median Absolute Deviation (MAD)1
Skewness1.8203436
Sum1849
Variance1.6756584
MonotonicityNot monotonic
2024-01-03T22:35:45.556035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 950
49.7%
1 460
24.0%
2 288
 
15.1%
3 122
 
6.4%
4 53
 
2.8%
5 21
 
1.1%
6 9
 
0.5%
7 6
 
0.3%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
0 950
49.7%
1 460
24.0%
2 288
 
15.1%
3 122
 
6.4%
4 53
 
2.8%
5 21
 
1.1%
6 9
 
0.5%
7 6
 
0.3%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 6
 
0.3%
6 9
 
0.5%
5 21
 
1.1%
4 53
 
2.8%
3 122
 
6.4%
2 288
 
15.1%
1 460
24.0%
0 950
49.7%

HOBBIES_1_006_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85833769
Minimum0
Maximum10
Zeros1261
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.624286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.571951
Coefficient of variation (CV)1.83139
Kurtosis5.8793317
Mean0.85833769
Median Absolute Deviation (MAD)0
Skewness2.3310588
Sum1642
Variance2.4710301
MonotonicityNot monotonic
2024-01-03T22:35:45.699338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1261
65.9%
1 232
 
12.1%
2 174
 
9.1%
3 107
 
5.6%
4 55
 
2.9%
5 32
 
1.7%
6 23
 
1.2%
7 15
 
0.8%
8 9
 
0.5%
9 4
 
0.2%
ValueCountFrequency (%)
0 1261
65.9%
1 232
 
12.1%
2 174
 
9.1%
3 107
 
5.6%
4 55
 
2.9%
5 32
 
1.7%
6 23
 
1.2%
7 15
 
0.8%
8 9
 
0.5%
9 4
 
0.2%
ValueCountFrequency (%)
10 1
 
0.1%
9 4
 
0.2%
8 9
 
0.5%
7 15
 
0.8%
6 23
 
1.2%
5 32
 
1.7%
4 55
 
2.9%
3 107
5.6%
2 174
9.1%
1 232
12.1%

HOBBIES_1_007_CA_1_validation
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1559 
1
292 
2
 
54
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

Length

2024-01-03T22:35:45.777199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:45.877558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1559
81.5%
1 292
 
15.3%
2 54
 
2.8%
3 8
 
0.4%

HOBBIES_1_008_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2294825
Minimum0
Maximum91
Zeros535
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:45.978171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q310
95-th percentile26
Maximum91
Range91
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1162947
Coefficient of variation (CV)1.2609886
Kurtosis8.9079924
Mean7.2294825
Median Absolute Deviation (MAD)4
Skewness2.3449744
Sum13830
Variance83.106829
MonotonicityNot monotonic
2024-01-03T22:35:46.071307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 535
28.0%
4 122
 
6.4%
2 122
 
6.4%
5 103
 
5.4%
6 97
 
5.1%
3 96
 
5.0%
1 91
 
4.8%
8 84
 
4.4%
7 73
 
3.8%
9 67
 
3.5%
Other values (44) 523
27.3%
ValueCountFrequency (%)
0 535
28.0%
1 91
 
4.8%
2 122
 
6.4%
3 96
 
5.0%
4 122
 
6.4%
5 103
 
5.4%
6 97
 
5.1%
7 73
 
3.8%
8 84
 
4.4%
9 67
 
3.5%
ValueCountFrequency (%)
91 1
 
0.1%
75 1
 
0.1%
57 1
 
0.1%
54 1
 
0.1%
52 1
 
0.1%
51 1
 
0.1%
50 1
 
0.1%
49 2
0.1%
48 2
0.1%
47 3
0.2%

HOBBIES_1_009_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1860951
Minimum0
Maximum20
Zeros1033
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:46.158929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0150779
Coefficient of variation (CV)1.6989176
Kurtosis14.842781
Mean1.1860951
Median Absolute Deviation (MAD)0
Skewness3.1315998
Sum2269
Variance4.0605388
MonotonicityNot monotonic
2024-01-03T22:35:46.246481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 1033
54.0%
1 350
 
18.3%
2 240
 
12.5%
3 104
 
5.4%
4 75
 
3.9%
5 28
 
1.5%
8 24
 
1.3%
6 22
 
1.2%
7 14
 
0.7%
9 8
 
0.4%
Other values (7) 15
 
0.8%
ValueCountFrequency (%)
0 1033
54.0%
1 350
 
18.3%
2 240
 
12.5%
3 104
 
5.4%
4 75
 
3.9%
5 28
 
1.5%
6 22
 
1.2%
7 14
 
0.7%
8 24
 
1.3%
9 8
 
0.4%
ValueCountFrequency (%)
20 1
 
0.1%
18 1
 
0.1%
16 3
 
0.2%
13 1
 
0.1%
12 1
 
0.1%
11 2
 
0.1%
10 6
 
0.3%
9 8
 
0.4%
8 24
1.3%
7 14
0.7%

HOBBIES_1_010_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71928907
Minimum0
Maximum6
Zeros996
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:46.337306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92168728
Coefficient of variation (CV)1.2813865
Kurtosis2.2497095
Mean0.71928907
Median Absolute Deviation (MAD)0
Skewness1.4103416
Sum1376
Variance0.84950744
MonotonicityNot monotonic
2024-01-03T22:35:46.404470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 996
52.1%
1 582
30.4%
2 239
 
12.5%
3 74
 
3.9%
4 18
 
0.9%
6 2
 
0.1%
5 2
 
0.1%
ValueCountFrequency (%)
0 996
52.1%
1 582
30.4%
2 239
 
12.5%
3 74
 
3.9%
4 18
 
0.9%
5 2
 
0.1%
6 2
 
0.1%
ValueCountFrequency (%)
6 2
 
0.1%
5 2
 
0.1%
4 18
 
0.9%
3 74
 
3.9%
2 239
 
12.5%
1 582
30.4%
0 996
52.1%

HOBBIES_1_011_CA_1_validation
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1784 
1
 
117
2
 
11
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%

Length

2024-01-03T22:35:46.503057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:46.609143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1784
93.3%
1 117
 
6.1%
2 11
 
0.6%
3 1
 
0.1%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1315 
1
461 
2
 
118
3
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

Length

2024-01-03T22:35:46.682657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:46.771891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1315
68.7%
1 461
 
24.1%
2 118
 
6.2%
3 19
 
1.0%

HOBBIES_1_013_CA_1_validation
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1523 
1
304 
2
 
72
3
 
11
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

Length

2024-01-03T22:35:46.851082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:46.948461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1523
79.6%
1 304
 
15.9%
2 72
 
3.8%
3 11
 
0.6%
4 3
 
0.2%

HOBBIES_1_014_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2535285
Minimum0
Maximum15
Zeros852
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:47.023245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5725214
Coefficient of variation (CV)1.254476
Kurtosis5.2980325
Mean1.2535285
Median Absolute Deviation (MAD)1
Skewness1.7710312
Sum2398
Variance2.4728236
MonotonicityNot monotonic
2024-01-03T22:35:47.094068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 852
44.5%
1 402
21.0%
2 303
 
15.8%
3 194
 
10.1%
4 83
 
4.3%
5 37
 
1.9%
6 22
 
1.2%
7 11
 
0.6%
8 6
 
0.3%
9 1
 
0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 852
44.5%
1 402
21.0%
2 303
 
15.8%
3 194
 
10.1%
4 83
 
4.3%
5 37
 
1.9%
6 22
 
1.2%
7 11
 
0.6%
8 6
 
0.3%
9 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
10 1
 
0.1%
9 1
 
0.1%
8 6
 
0.3%
7 11
 
0.6%
6 22
 
1.2%
5 37
 
1.9%
4 83
 
4.3%
3 194
10.1%
2 303
15.8%

HOBBIES_1_015_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0648197
Minimum0
Maximum83
Zeros373
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:47.182032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile20
Maximum83
Range83
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3533772
Coefficient of variation (CV)1.2124643
Kurtosis12.561413
Mean6.0648197
Median Absolute Deviation (MAD)3
Skewness2.6854024
Sum11602
Variance54.072156
MonotonicityNot monotonic
2024-01-03T22:35:47.276183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 373
19.5%
2 193
10.1%
3 172
9.0%
4 169
8.8%
1 167
8.7%
6 124
 
6.5%
5 104
 
5.4%
7 82
 
4.3%
8 71
 
3.7%
9 61
 
3.2%
Other values (35) 397
20.8%
ValueCountFrequency (%)
0 373
19.5%
1 167
8.7%
2 193
10.1%
3 172
9.0%
4 169
8.8%
5 104
 
5.4%
6 124
 
6.5%
7 82
 
4.3%
8 71
 
3.7%
9 61
 
3.2%
ValueCountFrequency (%)
83 1
0.1%
53 2
0.1%
52 1
0.1%
50 1
0.1%
49 1
0.1%
46 1
0.1%
43 1
0.1%
42 1
0.1%
40 2
0.1%
38 2
0.1%

HOBBIES_1_016_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5279665
Minimum0
Maximum90
Zeros393
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:47.406567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile18
Maximum90
Range90
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.7581363
Coefficient of variation (CV)1.403434
Kurtosis25.994741
Mean5.5279665
Median Absolute Deviation (MAD)3
Skewness4.0569215
Sum10575
Variance60.188679
MonotonicityNot monotonic
2024-01-03T22:35:47.738884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 393
20.5%
3 210
11.0%
1 196
10.2%
2 192
10.0%
4 154
 
8.1%
5 134
 
7.0%
6 102
 
5.3%
7 97
 
5.1%
8 75
 
3.9%
10 49
 
2.6%
Other values (41) 311
16.3%
ValueCountFrequency (%)
0 393
20.5%
1 196
10.2%
2 192
10.0%
3 210
11.0%
4 154
 
8.1%
5 134
 
7.0%
6 102
 
5.3%
7 97
 
5.1%
8 75
 
3.9%
9 40
 
2.1%
ValueCountFrequency (%)
90 1
0.1%
77 1
0.1%
73 1
0.1%
69 1
0.1%
64 1
0.1%
61 1
0.1%
60 1
0.1%
59 1
0.1%
56 1
0.1%
54 1
0.1%

HOBBIES_1_017_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.050183
Minimum0
Maximum14
Zeros963
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:47.829709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4645716
Coefficient of variation (CV)1.3945871
Kurtosis6.7107309
Mean1.050183
Median Absolute Deviation (MAD)0
Skewness2.053988
Sum2009
Variance2.1449699
MonotonicityNot monotonic
2024-01-03T22:35:47.940485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 963
50.3%
1 417
21.8%
2 258
 
13.5%
3 151
 
7.9%
4 59
 
3.1%
5 35
 
1.8%
6 17
 
0.9%
7 4
 
0.2%
8 4
 
0.2%
9 4
 
0.2%
ValueCountFrequency (%)
0 963
50.3%
1 417
21.8%
2 258
 
13.5%
3 151
 
7.9%
4 59
 
3.1%
5 35
 
1.8%
6 17
 
0.9%
7 4
 
0.2%
8 4
 
0.2%
9 4
 
0.2%
ValueCountFrequency (%)
14 1
 
0.1%
9 4
 
0.2%
8 4
 
0.2%
7 4
 
0.2%
6 17
 
0.9%
5 35
 
1.8%
4 59
 
3.1%
3 151
 
7.9%
2 258
13.5%
1 417
21.8%

HOBBIES_1_018_CA_1_validation
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1834 
1
 
71
2
 
7
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

Length

2024-01-03T22:35:48.023259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:48.124302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1834
95.9%
1 71
 
3.7%
2 7
 
0.4%
3 1
 
0.1%

HOBBIES_1_019_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7318348
Minimum0
Maximum64
Zeros995
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:48.234064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile22
Maximum64
Range64
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.6470053
Coefficient of variation (CV)1.6160761
Kurtosis6.7846335
Mean4.7318348
Median Absolute Deviation (MAD)0
Skewness2.3209469
Sum9052
Variance58.47669
MonotonicityNot monotonic
2024-01-03T22:35:48.357710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 995
52.0%
2 82
 
4.3%
4 77
 
4.0%
5 71
 
3.7%
8 65
 
3.4%
6 64
 
3.3%
1 57
 
3.0%
7 55
 
2.9%
3 54
 
2.8%
9 51
 
2.7%
Other values (35) 342
 
17.9%
ValueCountFrequency (%)
0 995
52.0%
1 57
 
3.0%
2 82
 
4.3%
3 54
 
2.8%
4 77
 
4.0%
5 71
 
3.7%
6 64
 
3.3%
7 55
 
2.9%
8 65
 
3.4%
9 51
 
2.7%
ValueCountFrequency (%)
64 1
0.1%
50 1
0.1%
48 2
0.1%
47 1
0.1%
43 1
0.1%
41 1
0.1%
38 1
0.1%
37 2
0.1%
36 2
0.1%
35 2
0.1%

HOBBIES_1_020_CA_1_validation
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1422 
1
391 
2
 
80
3
 
16
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

Length

2024-01-03T22:35:48.446898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:48.536401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1422
74.3%
1 391
 
20.4%
2 80
 
4.2%
3 16
 
0.8%
4 4
 
0.2%

HOBBIES_1_021_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65969681
Minimum0
Maximum11
Zeros1155
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:48.625964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0568225
Coefficient of variation (CV)1.6019821
Kurtosis10.880699
Mean0.65969681
Median Absolute Deviation (MAD)0
Skewness2.5241259
Sum1262
Variance1.1168738
MonotonicityNot monotonic
2024-01-03T22:35:48.707895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1155
60.4%
1 444
 
23.2%
2 200
 
10.5%
3 76
 
4.0%
4 22
 
1.2%
6 6
 
0.3%
5 5
 
0.3%
7 2
 
0.1%
8 2
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
0 1155
60.4%
1 444
 
23.2%
2 200
 
10.5%
3 76
 
4.0%
4 22
 
1.2%
5 5
 
0.3%
6 6
 
0.3%
7 2
 
0.1%
8 2
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
8 2
 
0.1%
7 2
 
0.1%
6 6
 
0.3%
5 5
 
0.3%
4 22
 
1.2%
3 76
 
4.0%
2 200
 
10.5%
1 444
 
23.2%
0 1155
60.4%

HOBBIES_1_022_CA_1_validation
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1396 
1
403 
2
 
94
3
 
19
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

Length

2024-01-03T22:35:48.793703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:48.895440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1396
73.0%
1 403
 
21.1%
2 94
 
4.9%
3 19
 
1.0%
5 1
 
0.1%

HOBBIES_1_023_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1233664
Minimum0
Maximum8
Zeros896
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:48.977338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.400255
Coefficient of variation (CV)1.2464811
Kurtosis2.0494124
Mean1.1233664
Median Absolute Deviation (MAD)1
Skewness1.4056213
Sum2149
Variance1.9607142
MonotonicityNot monotonic
2024-01-03T22:35:49.079080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 896
46.8%
1 415
21.7%
2 286
 
15.0%
3 183
 
9.6%
4 86
 
4.5%
5 25
 
1.3%
6 14
 
0.7%
7 4
 
0.2%
8 4
 
0.2%
ValueCountFrequency (%)
0 896
46.8%
1 415
21.7%
2 286
 
15.0%
3 183
 
9.6%
4 86
 
4.5%
5 25
 
1.3%
6 14
 
0.7%
7 4
 
0.2%
8 4
 
0.2%
ValueCountFrequency (%)
8 4
 
0.2%
7 4
 
0.2%
6 14
 
0.7%
5 25
 
1.3%
4 86
 
4.5%
3 183
 
9.6%
2 286
 
15.0%
1 415
21.7%
0 896
46.8%

HOBBIES_1_024_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24411918
Minimum0
Maximum5
Zeros1573
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:49.181023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.59942281
Coefficient of variation (CV)2.4554515
Kurtosis10.403514
Mean0.24411918
Median Absolute Deviation (MAD)0
Skewness2.9803834
Sum467
Variance0.35930771
MonotonicityNot monotonic
2024-01-03T22:35:49.257430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1573
82.2%
1 246
 
12.9%
2 67
 
3.5%
3 22
 
1.2%
4 4
 
0.2%
5 1
 
0.1%
ValueCountFrequency (%)
0 1573
82.2%
1 246
 
12.9%
2 67
 
3.5%
3 22
 
1.2%
4 4
 
0.2%
5 1
 
0.1%
ValueCountFrequency (%)
5 1
 
0.1%
4 4
 
0.2%
3 22
 
1.2%
2 67
 
3.5%
1 246
 
12.9%
0 1573
82.2%

HOBBIES_1_025_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47412441
Minimum0
Maximum6
Zeros1360
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:49.346838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92358938
Coefficient of variation (CV)1.9479895
Kurtosis7.855791
Mean0.47412441
Median Absolute Deviation (MAD)0
Skewness2.5700024
Sum907
Variance0.85301734
MonotonicityNot monotonic
2024-01-03T22:35:49.404405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1360
71.1%
1 338
 
17.7%
2 135
 
7.1%
3 41
 
2.1%
4 24
 
1.3%
5 10
 
0.5%
6 5
 
0.3%
ValueCountFrequency (%)
0 1360
71.1%
1 338
 
17.7%
2 135
 
7.1%
3 41
 
2.1%
4 24
 
1.3%
5 10
 
0.5%
6 5
 
0.3%
ValueCountFrequency (%)
6 5
 
0.3%
5 10
 
0.5%
4 24
 
1.3%
3 41
 
2.1%
2 135
 
7.1%
1 338
 
17.7%
0 1360
71.1%

HOBBIES_1_026_CA_1_validation
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
1894 
1
 
18
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1913
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

Length

2024-01-03T22:35:49.503913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T22:35:49.603179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1913
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1894
99.0%
1 18
 
0.9%
3 1
 
0.1%

HOBBIES_1_027_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19236801
Minimum0
Maximum6
Zeros1650
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:49.706776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55323723
Coefficient of variation (CV)2.8759316
Kurtosis18.743627
Mean0.19236801
Median Absolute Deviation (MAD)0
Skewness3.7618366
Sum368
Variance0.30607143
MonotonicityNot monotonic
2024-01-03T22:35:49.775952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1650
86.3%
1 185
 
9.7%
2 59
 
3.1%
3 14
 
0.7%
4 3
 
0.2%
5 1
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
0 1650
86.3%
1 185
 
9.7%
2 59
 
3.1%
3 14
 
0.7%
4 3
 
0.2%
5 1
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 1
 
0.1%
4 3
 
0.2%
3 14
 
0.7%
2 59
 
3.1%
1 185
 
9.7%
0 1650
86.3%

HOBBIES_1_028_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7009932
Minimum0
Maximum5
Zeros1068
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:49.861205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97214557
Coefficient of variation (CV)1.3868117
Kurtosis2.4983394
Mean0.7009932
Median Absolute Deviation (MAD)0
Skewness1.5603075
Sum1341
Variance0.945067
MonotonicityNot monotonic
2024-01-03T22:35:49.948944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1068
55.8%
1 500
26.1%
2 240
 
12.5%
3 69
 
3.6%
4 26
 
1.4%
5 10
 
0.5%
ValueCountFrequency (%)
0 1068
55.8%
1 500
26.1%
2 240
 
12.5%
3 69
 
3.6%
4 26
 
1.4%
5 10
 
0.5%
ValueCountFrequency (%)
5 10
 
0.5%
4 26
 
1.4%
3 69
 
3.6%
2 240
 
12.5%
1 500
26.1%
0 1068
55.8%

HOBBIES_1_029_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4704652
Minimum0
Maximum11
Zeros631
Zeros (%)33.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:50.028779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5007985
Coefficient of variation (CV)1.0206283
Kurtosis2.3529523
Mean1.4704652
Median Absolute Deviation (MAD)1
Skewness1.2784694
Sum2813
Variance2.2523961
MonotonicityNot monotonic
2024-01-03T22:35:50.104068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 631
33.0%
1 473
24.7%
2 406
21.2%
3 218
 
11.4%
4 114
 
6.0%
5 37
 
1.9%
6 15
 
0.8%
7 13
 
0.7%
8 4
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
0 631
33.0%
1 473
24.7%
2 406
21.2%
3 218
 
11.4%
4 114
 
6.0%
5 37
 
1.9%
6 15
 
0.8%
7 13
 
0.7%
8 4
 
0.2%
9 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
9 1
 
0.1%
8 4
 
0.2%
7 13
 
0.7%
6 15
 
0.8%
5 37
 
1.9%
4 114
 
6.0%
3 218
11.4%
2 406
21.2%
1 473
24.7%

HOBBIES_1_030_CA_1_validation
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9268165
Minimum0
Maximum52
Zeros930
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2024-01-03T22:35:50.190916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile13
Maximum52
Range52
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7643065
Coefficient of variation (CV)1.6278118
Kurtosis11.301953
Mean2.9268165
Median Absolute Deviation (MAD)1
Skewness2.7096081
Sum5599
Variance22.698616
MonotonicityNot monotonic
2024-01-03T22:35:50.286121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 930
48.6%
1 174
 
9.1%
2 143
 
7.5%
3 109
 
5.7%
4 108
 
5.6%
5 92
 
4.8%
6 71
 
3.7%
7 53
 
2.8%
8 34
 
1.8%
12 25
 
1.3%
Other values (21) 174
 
9.1%
ValueCountFrequency (%)
0 930
48.6%
1 174
 
9.1%
2 143
 
7.5%
3 109
 
5.7%
4 108
 
5.6%
5 92
 
4.8%
6 71
 
3.7%
7 53
 
2.8%
8 34
 
1.8%
9 25
 
1.3%
ValueCountFrequency (%)
52 1
 
0.1%
32 1
 
0.1%
31 1
 
0.1%
29 1
 
0.1%
28 1
 
0.1%
25 5
0.3%
24 4
0.2%
23 2
 
0.1%
22 3
0.2%
21 5
0.3%

date_col
Date

UNIQUE 

Distinct1913
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Minimum2011-01-29 00:00:00
Maximum2016-04-24 00:00:00
2024-01-03T22:35:50.376990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:50.475875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-03T22:35:42.000545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:00.900377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:02.803541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:04.866536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:06.888466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:08.904620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:10.753761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:12.682625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:14.561248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:16.580801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:18.503391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:20.455284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:22.471989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:24.720060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:26.626560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:28.445423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:30.497700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:32.488126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:34.390806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:36.447111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:38.336473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:40.100401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:42.088170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:00.970123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:02.895464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:04.957386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:06.968482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:08.983384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:10.844698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:12.767284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:14.650151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:16.665865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:18.584069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:20.564559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:22.550632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:24.818378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:26.703188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:28.520951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:30.600584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:32.572623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:34.475271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:36.526182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:38.411581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:40.180257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:42.181853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:01.072737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:02.992765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:05.050390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:07.054213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:09.058452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:10.918811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:12.851810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:14.749808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:16.748794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:18.657260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:20.662123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:22.618792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:24.905089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:26.792520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:28.595781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:30.686480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:32.637891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:34.561643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-01-03T22:35:02.691264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:04.772199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:06.777563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:08.805400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:10.673216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:12.599852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:14.484347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:16.485180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:18.419838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:20.364156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:22.396598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:24.661577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:26.519659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:28.370743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:30.406204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:32.396649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:34.312160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:36.356385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:38.249752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:40.014764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-03T22:35:41.903324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-03T22:35:50.586511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
HOBBIES_1_001_CA_1_validationHOBBIES_1_002_CA_1_validationHOBBIES_1_003_CA_1_validationHOBBIES_1_004_CA_1_validationHOBBIES_1_005_CA_1_validationHOBBIES_1_006_CA_1_validationHOBBIES_1_007_CA_1_validationHOBBIES_1_008_CA_1_validationHOBBIES_1_009_CA_1_validationHOBBIES_1_010_CA_1_validationHOBBIES_1_011_CA_1_validationHOBBIES_1_012_CA_1_validationHOBBIES_1_013_CA_1_validationHOBBIES_1_014_CA_1_validationHOBBIES_1_015_CA_1_validationHOBBIES_1_016_CA_1_validationHOBBIES_1_017_CA_1_validationHOBBIES_1_018_CA_1_validationHOBBIES_1_019_CA_1_validationHOBBIES_1_020_CA_1_validationHOBBIES_1_021_CA_1_validationHOBBIES_1_022_CA_1_validationHOBBIES_1_023_CA_1_validationHOBBIES_1_024_CA_1_validationHOBBIES_1_025_CA_1_validationHOBBIES_1_026_CA_1_validationHOBBIES_1_027_CA_1_validationHOBBIES_1_028_CA_1_validationHOBBIES_1_029_CA_1_validationHOBBIES_1_030_CA_1_validation
HOBBIES_1_001_CA_1_validation1.0000.0640.2140.1650.089-0.0140.0430.142-0.153-0.0230.0380.0000.0410.1360.0060.0770.1080.0000.2880.0000.1030.000-0.1130.0620.0850.0690.216-0.0870.0440.305
HOBBIES_1_002_CA_1_validation0.0641.0000.0370.0120.1190.0470.0510.028-0.0570.0220.0000.0000.0470.0680.0330.0190.0750.0000.0460.0700.0170.000-0.0140.0120.0270.0000.0760.0060.0310.069
HOBBIES_1_003_CA_1_validation0.2140.0371.0000.0850.081-0.0400.0000.124-0.151-0.0040.0000.0210.0280.136-0.0550.0740.0380.0400.1360.0510.0700.000-0.0780.0120.0890.0000.235-0.0680.0290.214
HOBBIES_1_004_CA_1_validation0.1650.0120.0851.0000.0610.0020.0000.141-0.0100.0950.0000.0000.0700.1100.1520.1180.0890.0260.1540.0000.0960.018-0.0370.0420.1390.0000.099-0.0120.0790.188
HOBBIES_1_005_CA_1_validation0.0890.1190.0810.0611.0000.0540.0830.050-0.0460.0480.0000.0000.0420.0820.0300.0430.0730.0000.1410.007-0.0100.0240.0140.1130.0820.0000.0890.0110.0600.144
HOBBIES_1_006_CA_1_validation-0.0140.047-0.0400.0020.0541.0000.047-0.055-0.0070.1130.1780.0000.0000.120-0.012-0.0260.1220.0170.0820.000-0.0100.000-0.0300.0910.0000.073-0.015-0.099-0.0440.109
HOBBIES_1_007_CA_1_validation0.0430.0510.0000.0000.0830.0471.0000.054-0.071-0.0130.0340.0200.0670.118-0.006-0.0050.0700.0000.2230.0000.0950.000-0.0220.0630.0460.0000.046-0.0730.0250.205
HOBBIES_1_008_CA_1_validation0.1420.0280.1240.1410.050-0.0550.0541.000-0.0610.0260.0000.0000.0000.0440.0390.0720.0430.0000.2920.0000.1210.000-0.1000.0140.0890.0000.155-0.0200.0090.164
HOBBIES_1_009_CA_1_validation-0.153-0.057-0.151-0.010-0.046-0.007-0.071-0.0611.0000.0360.0000.1340.000-0.0690.0570.0020.0050.000-0.1230.000-0.0450.0000.074-0.014-0.0150.000-0.1840.028-0.028-0.130
HOBBIES_1_010_CA_1_validation-0.0230.022-0.0040.0950.0480.113-0.0130.0260.0361.0000.0000.0860.0190.0530.0420.0180.1430.0690.0060.000-0.0400.0000.0210.0250.0870.000-0.0240.0090.0170.045
HOBBIES_1_011_CA_1_validation0.0380.0000.0000.0000.0000.1780.0340.0000.0000.0001.0000.0000.0160.0280.024-0.0150.0540.0380.0210.000-0.0290.0180.0520.0670.0520.0000.035-0.0100.0530.087
HOBBIES_1_012_CA_1_validation0.0000.0000.0210.0000.0000.0000.0200.0000.1340.0860.0001.0000.012-0.0560.020-0.0160.0050.000-0.0320.011-0.0170.0310.049-0.028-0.0200.161-0.0840.0350.003-0.062
HOBBIES_1_013_CA_1_validation0.0410.0470.0280.0700.0420.0000.0670.0000.0000.0190.0160.0121.0000.078-0.0090.0420.0730.0000.1780.0000.0500.000-0.0410.1040.0530.0000.070-0.0900.0690.188
HOBBIES_1_014_CA_1_validation0.1360.0680.1360.1100.0820.1200.1180.044-0.0690.0530.028-0.0560.0781.0000.0040.0060.1200.0000.1850.000-0.0000.000-0.1020.1000.0890.0550.155-0.072-0.0090.235
HOBBIES_1_015_CA_1_validation0.0060.033-0.0550.1520.030-0.012-0.0060.0390.0570.0420.0240.020-0.0090.0041.0000.1260.0610.0000.0300.0540.0440.0000.0100.0330.0660.000-0.0500.0670.0220.057
HOBBIES_1_016_CA_1_validation0.0770.0190.0740.1180.043-0.026-0.0050.0720.0020.018-0.015-0.0160.0420.0060.1261.0000.0300.0530.0740.1690.0510.0370.0130.0310.0540.1510.095-0.0200.0670.147
HOBBIES_1_017_CA_1_validation0.1080.0750.0380.0890.0730.1220.0700.0430.0050.1430.0540.0050.0730.1200.0610.0301.0000.0000.0750.000-0.0790.0000.0010.0430.1490.0000.030-0.070-0.0030.151
HOBBIES_1_018_CA_1_validation0.0000.0000.0400.0260.0000.0170.0000.0000.0000.0690.0380.0000.0000.0000.0000.0530.0001.0000.0060.0000.0210.0000.017-0.0210.0240.0000.036-0.0850.0390.053
HOBBIES_1_019_CA_1_validation0.2880.0460.1360.1540.1410.0820.2230.292-0.1230.0060.021-0.0320.1780.1850.0300.0740.0750.0061.0000.0000.2060.000-0.1150.1500.1200.0000.145-0.1730.0620.410
HOBBIES_1_020_CA_1_validation0.0000.0700.0510.0000.0070.0000.0000.0000.0000.0000.0000.0110.0000.0000.0540.1690.0000.0000.0001.0000.0200.0000.034-0.027-0.0200.000-0.0160.0490.022-0.089
HOBBIES_1_021_CA_1_validation0.1030.0170.0700.096-0.010-0.0100.0950.121-0.045-0.040-0.029-0.0170.050-0.0000.0440.051-0.0790.0210.2060.0201.0000.0000.0260.0280.0110.0000.0510.0250.0260.122
HOBBIES_1_022_CA_1_validation0.0000.0000.0000.0180.0240.0000.0000.0000.0000.0000.0180.0310.0000.0000.0000.0370.0000.0000.0000.0000.0001.0000.135-0.028-0.0200.060-0.0510.0740.032-0.053
HOBBIES_1_023_CA_1_validation-0.113-0.014-0.078-0.0370.014-0.030-0.022-0.1000.0740.0210.0520.049-0.041-0.1020.0100.0130.0010.017-0.1150.0340.0260.1351.000-0.064-0.0140.000-0.1460.1380.055-0.149
HOBBIES_1_024_CA_1_validation0.0620.0120.0120.0420.1130.0910.0630.014-0.0140.0250.067-0.0280.1040.1000.0330.0310.043-0.0210.150-0.0270.028-0.028-0.0641.0000.0120.0000.089-0.060-0.0170.191
HOBBIES_1_025_CA_1_validation0.0850.0270.0890.1390.0820.0000.0460.089-0.0150.0870.052-0.0200.0530.0890.0660.0540.1490.0240.120-0.0200.011-0.020-0.0140.0121.0000.0000.080-0.0870.0190.115
HOBBIES_1_026_CA_1_validation0.0690.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.1610.0000.0550.0000.1510.0000.0000.0000.0000.0000.0600.0000.0000.0001.0000.037-0.0060.0000.055
HOBBIES_1_027_CA_1_validation0.2160.0760.2350.0990.089-0.0150.0460.155-0.184-0.0240.035-0.0840.0700.155-0.0500.0950.0300.0360.145-0.0160.051-0.051-0.1460.0890.0800.0371.000-0.0730.0450.241
HOBBIES_1_028_CA_1_validation-0.0870.006-0.068-0.0120.011-0.099-0.073-0.0200.0280.009-0.0100.035-0.090-0.0720.067-0.020-0.070-0.085-0.1730.0490.0250.0740.138-0.060-0.087-0.006-0.0731.0000.034-0.133
HOBBIES_1_029_CA_1_validation0.0440.0310.0290.0790.060-0.0440.0250.009-0.0280.0170.0530.0030.069-0.0090.0220.067-0.0030.0390.0620.0220.0260.0320.055-0.0170.0190.0000.0450.0341.0000.086
HOBBIES_1_030_CA_1_validation0.3050.0690.2140.1880.1440.1090.2050.164-0.1300.0450.087-0.0620.1880.2350.0570.1470.1510.0530.410-0.0890.122-0.053-0.1490.1910.1150.0550.241-0.1330.0861.000

Missing values

2024-01-03T22:35:44.168134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-03T22:35:44.539599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

HOBBIES_1_001_CA_1_validationHOBBIES_1_002_CA_1_validationHOBBIES_1_003_CA_1_validationHOBBIES_1_004_CA_1_validationHOBBIES_1_005_CA_1_validationHOBBIES_1_006_CA_1_validationHOBBIES_1_007_CA_1_validationHOBBIES_1_008_CA_1_validationHOBBIES_1_009_CA_1_validationHOBBIES_1_010_CA_1_validationHOBBIES_1_011_CA_1_validationHOBBIES_1_012_CA_1_validationHOBBIES_1_013_CA_1_validationHOBBIES_1_014_CA_1_validationHOBBIES_1_015_CA_1_validationHOBBIES_1_016_CA_1_validationHOBBIES_1_017_CA_1_validationHOBBIES_1_018_CA_1_validationHOBBIES_1_019_CA_1_validationHOBBIES_1_020_CA_1_validationHOBBIES_1_021_CA_1_validationHOBBIES_1_022_CA_1_validationHOBBIES_1_023_CA_1_validationHOBBIES_1_024_CA_1_validationHOBBIES_1_025_CA_1_validationHOBBIES_1_026_CA_1_validationHOBBIES_1_027_CA_1_validationHOBBIES_1_028_CA_1_validationHOBBIES_1_029_CA_1_validationHOBBIES_1_030_CA_1_validationdate_col
2011-01-2900000001220000045000002200000202011-01-29
2011-01-3000000001500020001000001100000002011-01-30
2011-01-310000000071000003000001000000302011-01-31
2011-02-010000000030000050000001000000002011-02-01
2011-02-0200000000000000015000000200001002011-02-02
2011-02-0300000004200000032000000100002202011-02-03
2011-02-040000000630000001000001300001202011-02-04
2011-02-050000000590020005000001700000002011-02-05
2011-02-060000000700000002000000200001002011-02-06
2011-02-070000000000000007000001000000002011-02-07
HOBBIES_1_001_CA_1_validationHOBBIES_1_002_CA_1_validationHOBBIES_1_003_CA_1_validationHOBBIES_1_004_CA_1_validationHOBBIES_1_005_CA_1_validationHOBBIES_1_006_CA_1_validationHOBBIES_1_007_CA_1_validationHOBBIES_1_008_CA_1_validationHOBBIES_1_009_CA_1_validationHOBBIES_1_010_CA_1_validationHOBBIES_1_011_CA_1_validationHOBBIES_1_012_CA_1_validationHOBBIES_1_013_CA_1_validationHOBBIES_1_014_CA_1_validationHOBBIES_1_015_CA_1_validationHOBBIES_1_016_CA_1_validationHOBBIES_1_017_CA_1_validationHOBBIES_1_018_CA_1_validationHOBBIES_1_019_CA_1_validationHOBBIES_1_020_CA_1_validationHOBBIES_1_021_CA_1_validationHOBBIES_1_022_CA_1_validationHOBBIES_1_023_CA_1_validationHOBBIES_1_024_CA_1_validationHOBBIES_1_025_CA_1_validationHOBBIES_1_026_CA_1_validationHOBBIES_1_027_CA_1_validationHOBBIES_1_028_CA_1_validationHOBBIES_1_029_CA_1_validationHOBBIES_1_030_CA_1_validationdate_col
2016-04-151021200001002015103100000002002016-04-15
2016-04-1630101100000002460031120200100002016-04-16
2016-04-170025100110112221500300311000005122016-04-17
2016-04-1810140113710001129000000301001102016-04-18
2016-04-191011100360000214005000000010282016-04-19
2016-04-201110101400000004000001100001172016-04-20
2016-04-2130012006000100000011001102001472016-04-21
2016-04-22001322030200032152040000220010192016-04-22
2016-04-231017201200001151106010402121032016-04-23
2016-04-24101240110200114660120001010013112016-04-24